Assessment of Automated Measurement and Verification (M&amp;V) Methods

Granderson, Jessica; Touzani, Samir; Custodio, Claudine; Sohn, Michael D.; Fernandes, Samuel; Jump, David

doi:10.2172/1236174

Cited by 21 publications

(10 citation statements)

References 8 publications

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“…(McLeod et al 2012) proposed a new method for the generation of current and future probabilistic micro regional climatic data in Passivhaus design, such data should provide a more robust basis for future cost and performance optimisation in low energy and passive building design. (Granderson et al 2015) reported documents the application of a general statistical methodology to assess the accuracy of baseline energy models, focusing on its application to Measurement and Verification (M&V) of whole-building energy savings. Low-cost energy saving and indoor comfort are the key directions for energy saving improvement in the future.…”

Section: Discussionmentioning

confidence: 99%

Applying the naïve Bayes classifier to HVAC energy prediction using hourly data

Lin

Liu

et al. 2019

Microsyst Technol

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Heating ventilation and air conditioning (HVAC) accounts for approximately 50% of the total energy consumption of buildings. Therefore, many studies have been focused on the simulation and optimal control of HVAC power consumption or the prediction of energy consumption through the construction of energy consumption models and the improvement of HVAC power consumption through energy management methods. The prediction of energy consumption by optimal energy-saving control or energy baseline is dependent on an accurate energy consumption model, however, the accuracy of the energy consumption model is influenced by the model variables. In addition, different operating periods and load conditions also lead to different changes in energy consumption, which will affect the accuracy of optimal energy consumption control or prediction of energy consumption. The present study proposes a method to enhance the accuracy and sensitivity of HVAC power consumption prediction, which involves the use of a clustering technique to locate clusters with similar information within hourly data, the construction of energy consumption models by converting the clustered hourly data into monthly data, and the application of the proposed Naïve Bayes classifier to classify hourly data under different operating conditions into the energy consumption model with the smallest prediction error. A multiple variable regression model and an artificial neural network (ANN) model were compared with the models developed in the present study, and the normalized mean bias error (NMBE) and the coefficient of variation of the root mean squared error (Cv-RMSE) were used as criteria for the predicted energy consumption values.

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Section: Discussionmentioning

confidence: 99%

Applying the naïve Bayes classifier to HVAC energy prediction using hourly data

Lin

Liu

et al. 2019

Microsyst Technol

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“…CV(RMSE) (Coefficient of Variation of the Root Mean Square Error) measures the variability of the errors between measured and simulated values. It "gives an indication of the model's ability to predict the overall load shape that is reflected in the data" [19]. In this case, the value of p is suggested to be one [17,18].…”

Section: Measuring Uncertaintymentioning

confidence: 99%

“…Such examples have been taken from the most important journals and documents in the energy field, in particular the documents that use building calibrated models in their research. Obviously, in this search, there is a wide variety of articles, reports and theses where N MBE is used correctly [17][18][19]. Table 3 is an overview of these documents ordered by error type, journal/report/thesis and year published.…”

Section: References and Journals Affectedmentioning

confidence: 99%

Validation of Calibrated Energy Models: Common Errors

2017

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Nowadays, there is growing interest in all the smart technologies that provide us with information and knowledge about the human environment. In the energy field, thanks to the amount of data received from smart meters and devices and the progress made in both energy software and computers, the quality of energy models is gradually improving and, hence, also the suitability of Energy Conservation Measures (ECMs). For this reason, the measurement of the accuracy of building energy models is an important task, because once the model is validated through a calibration procedure, it can be used, for example, to apply and study different strategies to reduce its energy consumption in maintaining human comfort. There are several agencies that have developed guidelines and methodologies to establish a measure of the accuracy of these models, and the most widely recognized are: ASHRAE Guideline 14-2014, the International Performance Measurement and Verification Protocol (IPMVP) and the Federal Energy Management Program (FEMP). This article intends to shed light on these validation measurements (uncertainty indices) by focusing on the typical mistakes made, as these errors could produce a false belief that the models used are calibrated.

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“…This predicted energy consumption is then compared to the actual energy consumption of the building that had undergone a retrofit, to calculate real energy savings. Automated M&V tools provide a more cost-effective and timely way to calculate energy savings after a building retrofit (Granderson et al 2015).…”

Section: Estimation Of Potential Energy Savingsmentioning

confidence: 99%

“…Efforts were conducted by Lawrence Berkeley National Laboratory (LBNL) to develop a testing procedure and metrics to assess the performance of automated measurement and verification (M&V) tools (Granderson et al 2015). However, automated M&V tools serve a different purpose and audience than no touch audit technologies; they predict energy savings after a retrofit, rather than identify probable causes of high energy use.…”

Section: Introductionmentioning

confidence: 99%